Search result for Mathematics for machine learning specialization Online Courses & Certifications
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Fundamentals of Machine Learning in Finance
by Igor Halperin- 3.8
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Approx. 18 hours to complete
Fundamentals of Machine Learning in Finance will provide more at-depth view of supervised, unsupervised, and reinforcement learning, and end up in a project on using unsupervised learning for implementing a simple portfolio trading strategy. Introduction to Fundamentals of Machine Learning in Finance Geron, “Hands-On Machine Learning with Scikit-Learn and TensorFlow”, Chapters 6 & 7...
Build Basic Generative Adversarial Networks (GANs)
by Sharon Zhou , Eda Zhou , Eric Zelikman- 4.7
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Approx. 31 hours to complete
This Specialization provides an accessible pathway for all levels of learners looking to break into the GANs space or apply GANs to their own projects, even without prior familiarity with advanced math and machine learning research. Welcome to the Specialization (Optional Notebook) GANs for Video...
Advanced Computer Vision with TensorFlow
by Laurence Moroney , Eddy Shyu- 4.8
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Approx. 24 hours to complete
Apply transfer learning to object localization and detection. This Specialization is for early and mid-career software and machine learning engineers with a foundational understanding of TensorFlow who are looking to expand their knowledge and skill set by learning advanced TensorFlow features to build powerful models. Options in Transfer Learning Transfer Learning with ResNet50 Network architecture for Object Localization...
Deep Learning and Reinforcement Learning
by Mark J Grover , Miguel Maldonado- 4.7
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Approx. 14 hours to complete
After this course, if you have followed the courses of the IBM Specialization in order, you will have considerable practice and a solid understanding in the main types of Machine Learning which are: Supervised Learning, Unsupervised Learning, Deep Learning, and Reinforcement Learning. Explain the kinds of problems suitable for Unsupervised Learning approaches Regularization Techniques for Deep Learning...
Machine Learning A-Z: Support Vector Machine with Python ©
by AI Sciences- 4.6
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11.5 hours on-demand video
The Complete Machine Learning and Support Vector Machine Course for Beginners We’ll also learn various steps of data preprocessing which allows us to make data ready for machine learning algorithms. Machine Learning concepts, including: This course is for you if you want to learn how to program in Python for Machine Learning...
$9.99
The Absolute Beginners Guide to Data Science
by Sai Acuity Institute of Learning Pvt Ltd Enabling Learning Through Insight!- 0.0
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42.5 hours on-demand video
Build your mathematics and statistics foundations strongly and ensure your Data science fundamentals are in place! Not only are Data Scientists responsible for business analytics, but they are also involved in building data products and software platforms, along with developing visualizations and machine learning algorithms Machine learning is everywhere. You will understand the mathematics and statistics behind Machine Learning...
$12.99
VSD - Machine Intelligence in EDA/CAD
by Kunal Ghosh- 4.2
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4 hours on-demand video
Listen from CEO/architect himself on Machine learning Then we will give overall introduction to categories of machine learning (supervised and unsupervised learning) and go about discussing that a little bit. He is passionate about many technical topics including Machine Learning, Analysis, Characterization and Modeling, which led him to architect guna - an advanced characterization software for modern nodes....
$12.99
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Build Decision Trees, SVMs, and Artificial Neural Networks
by Stacey McBrine- 0.0
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Approx. 22 hours to complete
There are numerous types of machine learning algorithms, each of which has certain characteristics that might make it more or less suitable for solving a particular problem. Ultimately, this course concludes the technical exploration of the various machine learning algorithms and how they can be used to build problem-solving models. Ensemble Learning...
Introduction to High-Performance and Parallel Computing
by Shelley Knuth , Thomas Hauser- 3.4
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Approx. 18 hours to complete
It is targeted to scientists, engineers, scholars, really everyone seeking to develop the software skills necessary for work in parallel software environments. These skills include big-data analysis, machine learning, parallel programming, and optimization. *Quantifying the processing, data, and cost requirements for a computational project or workflow High-Performance Computing (HPC) for Non-Computer Scientists...
Applied Machine Learning in R
by Bogdan Anastasiei- 4.5
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8 hours on-demand video
Get the essential machine learning skills and use them in real life situations So it may be the right time for you to enroll in this course and start building your machine learning competences today! After the first two introductory sections, we will get to study the supervised machine learning techniques....
$12.99
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